{"title":"使用市场和情绪变量分析比特币价格","authors":"Burcu Kapar, Jose Olmo","doi":"10.2139/ssrn.3691756","DOIUrl":null,"url":null,"abstract":"This paper proposes an empirical model for analyzing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010-2017 and 2010-2019. Price discovery is achieved through the Gonzalo-Granger permanent-transitory decomposition. The pricing factors are endogenous linear combinations of the S&P 500 index, gold price, a Google search variable associated to Bitcoin, and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period a linear combination of four pricing factors describes the efficient Bitcoin price. The S&P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalized by individuals’ search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018-2019 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Analysis of Bitcoin Prices Using Market and Sentiment Variables\",\"authors\":\"Burcu Kapar, Jose Olmo\",\"doi\":\"10.2139/ssrn.3691756\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes an empirical model for analyzing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010-2017 and 2010-2019. Price discovery is achieved through the Gonzalo-Granger permanent-transitory decomposition. The pricing factors are endogenous linear combinations of the S&P 500 index, gold price, a Google search variable associated to Bitcoin, and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period a linear combination of four pricing factors describes the efficient Bitcoin price. The S&P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalized by individuals’ search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018-2019 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.\",\"PeriodicalId\":209192,\"journal\":{\"name\":\"ERN: Asset Pricing Models (Topic)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Asset Pricing Models (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3691756\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Asset Pricing Models (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3691756","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis of Bitcoin Prices Using Market and Sentiment Variables
This paper proposes an empirical model for analyzing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010-2017 and 2010-2019. Price discovery is achieved through the Gonzalo-Granger permanent-transitory decomposition. The pricing factors are endogenous linear combinations of the S&P 500 index, gold price, a Google search variable associated to Bitcoin, and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period a linear combination of four pricing factors describes the efficient Bitcoin price. The S&P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalized by individuals’ search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018-2019 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.